A Model of Teacher Leadership Development on Competency-Based Learning Management for Teachers in Science and Technology Department in Large-Sized Secondary Schools under the Office of the Basic Education Commission in the Northeast
Bibliographic record
Abstract
The objectives of this Research and Development were to: Develop Competency-based learning management for Teachers of the science and technology Departments in large-sized secondary Schools under the office of the basic education commission in the northeast. The findings were as follows: I) There were five main components in Developing Teacher Leadership in Competency-based, namely 1) Process management as the basis for learning 2) Self-development and peer teachers 3) Development Course 4) Performance measurement and evaluation 5) Characterization as the teacher. II) The necessary needs of teacher leadership for competency-based learning management were as follows: 1) Performance measurement and evaluation PNI Modified = 0.842 and 2) Process management is the basis for learning PNI Modified = 0.297. III) A Model for Developing Teacher Leadership in Competency-based for use of the form, Origin, and significance of form, principle, purpose, The content of the format, The process of form supervision, and follow-up 4) Analysis of results. Pre-test and Post-test Score before training and after training difference are significant at.05.The results of the evaluation of the behavior and leadership of the teacher in the field of learning management and overall performance in the process of management and learning are very high (x=4.026, S.D.=0.699) and In terms of performance measurement and evaluation, the average value is very high (x=4.144, S.D.=0.710), statistically used by one sample t-test Compared with the average criterion of 3.51, it was found that the average score of teacher leadership behavior in the competency-based learning management was higher than the average of 3.51 with a statistical significance at .05.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".